Research on aerodynamic performance and noise reduction of high-voltage fans on fuel cell vehicles

Guo, Rong; Mi, Tiantian; Li, Lü; Luo, Ran

doi:10.1016/j.apacoust.2021.108454

Cited by 14 publications

(10 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When selecting a suitable algorithm for surrogate modeling, several factors need to be considered, such as the size, input format, and dimensionality of the dataset, the smoothness and nonlinearity of the function, and the need for prediction variance, according to the guidelines in Section 2. Statistical methods, such as PCE [238,239], polynomial response surface model (RSM) [240][241][242][243], RBF interpolation [244,245], lowrank tensor approximations [235], and spectral expansions, [231] are largely used to construct surrogates in SD&V. ML supervised regressors, such as SVM [246], GPR [247,248], NN [234], RF, and gradient-boosting decision trees [249], are also commonly employed due to their capabilities to approximate arbitrary functions, as they pose weak assumptions on the format of the underlying function.…”

Section: Surrogate Workflow and Related Methodsmentioning

confidence: 99%

“…For instance, surrogate-based optimization of the vehicle mass subjected to NVH and crashworthiness constraints was performed using RSM [279] and RBFbased interpolation [245]. Surrogate models based on quadratic polynomial regression were applied for NVH optimization in the fan of a fuel cell electric vehicle [240], in vehicle bodies [242,290] and to minimize structure-borne noise arising from general-purpose panels [241]. In [280], the acoustic optimization of an electric motor was tackled through local surrogates replacing FEM, and different ML algorithms were evaluated as surrogates, namely linear regression, decision tree, SVM, and GPR.…”

Section: Optimization With Surrogate Modelsmentioning

confidence: 99%

See 1 more Smart Citation

A review of machine learning methods applied to structural dynamics and vibroacoustic

Cunha

Droz

Zine

et al. 2023

Mechanical Systems and Signal Processing

View full text Add to dashboard Cite

Section: Surrogate Workflow and Related Methodsmentioning

confidence: 99%

Section: Optimization With Surrogate Modelsmentioning

confidence: 99%

A review of machine learning methods applied to structural dynamics and vibroacoustic

Cunha

Droz

Zine

et al. 2023

Mechanical Systems and Signal Processing

View full text Add to dashboard Cite

“…[169] carried out Bandgap optimization of meta-materials supported by RBF surrogate. A surrogate model based on quadratic polynomial regression also leveraged the aerodynamic and acoustic optimization of a fuel cell vehicle fan [325].…”

Section: Optimization With Surrogate Modelsmentioning

confidence: 99%

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

Cunha¹,

Droz²,

Zine³

et al. 2022

Preprint

View full text Add to dashboard Cite

The use of Machine Learning (ML) has rapidly spread across several fields, having encountered many applications in Structural Dynamics and Vibroacoustic (SD&V). The increasing capabilities of ML to unveil insights from data, driven by unprecedented data availability, algorithms advances and computational power, enhance decision making, uncertainty handling, patterns recognition and real-time assessments. Three main applications in SD&V have taken advantage of these benefits. In Structural Health Monitoring, ML detection and prognosis lead to safe operation and optimized maintenance schedules. System identification and control design are leveraged by ML techniques in Active Noise Control and Active Vibration Control. Finally, the so-called ML-based surrogate models provide fast alternatives to costly simulations, enabling robust and optimized product design. Despite the many works in the area, they have not been reviewed and analyzed. Therefore, to keep track and understand this ongoing integration of fields, this paper presents a survey of ML applications in SD&V analyses, shedding light on the current state of implementation and emerging opportunities. The main methodologies, advantages, limitations, and recommendations based on scientific knowledge were identified for each of the three applications. Moreover, the paper considers the role of Digital Twins and Physics Guided ML to overcome current challenges and power future research progress. As a result, the survey provides a broad overview of the present landscape of ML applied in SD&V and guides the reader to an advanced understanding of progress and prospects in the field.

show abstract

“…Surrogate models have been applied to the vibroacoustic domain as Noise, Vibration and Harshness (NVH) performance arises as a key indicator of customer satisfaction and vibroacoustic simulations are computationally costly due to, e.g., the complex behavior involved in fluid-structure interactions. In many studies, the system response is approximated by a polynomial using the Response Surface Methodology (RSM) [4][5][6][7][8][9]. Second-order polynomials are usually used in RSM for their sample efficiency and interpretability, but they are unable to capture arbitrary nonlinearities in the system response.…”

Section: Introductionmentioning

confidence: 99%

On Machine Learning-Driven Surrogates for Sound Transmission Loss Simulations

Cunha¹,

Zine²,

Ichchou³

et al. 2022

Preprint

View full text Add to dashboard Cite

Surrogate models are data-based approximations of computationally expensive simulations that enable efficient exploration of the model's design space and informed decision-making in many physical domains. The usage of surrogate models in the vibroacoustic domain, however, is challenging due to the non-smooth, complex behavior of wave phenomena. This paper investigates four Machine Learning (ML) approaches in the modelling of surrogates of Sound Transmission Loss (STL). Feature importance and feature engineering are used to improve the models' accuracy while increasing their interpretability and physical consistency. The transfer of the proposed techniques to other problems in the vibroacoustic domain and possible limitations of the models are discussed.

show abstract

Research on aerodynamic performance and noise reduction of high-voltage fans on fuel cell vehicles

Cited by 14 publications

References 22 publications

A review of machine learning methods applied to structural dynamics and vibroacoustic

A review of machine learning methods applied to structural dynamics and vibroacoustic

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

On Machine Learning-Driven Surrogates for Sound Transmission Loss Simulations

Contact Info

Product

Resources

About